Web Annotation as a First Class Object
Scholars have made handwritten notes and comments in books and manuscripts for centuries. Today's blogs and news sites typically invite users to express their opinions on the published content; URLs allow web resources to be shared with accompanying annotations and comments using third-party services like Twitter or Facebook. These contributions have until recently been constrained within specific services, making them second-class citizens of the Web. Web Annotations are now emerging as fully independent Linked Data in their own right, no longer restricted to plain textual comments in application silos. Annotations can now range from bookmarks and comments, to fine-grained annotations of a selection of, for example, a section of a frame within a video stream. Technologies and standards now exist to create, publish, syndicate, mash-up and consume, finely targeted, semantically rich digital annotations on practically any content, as first-class Web citizens. This development is being driven by the need for collaboration and annotation reuse amongst domain researchers, computer scientists, scientific publishers, and scholarly content databases.
Code (0)
등록된 구현이 없습니다.
Tasks
ObjectSimilar Papers 제목 키워드 기반
Faster Bounding Box Annotation for Object Detection in Indoor Scenes
This paper proposes an approach for rapid bounding box annotation for object detection datasets. The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step pro…
Objectobject-detectionObject DetectionObject Detection In Indoor ScenesEfficient Object Annotation via Speaking and Pointing
Deep neural networks deliver state-of-the-art visual recognition, but they rely on large datasets, which are time-consuming to annotate. These datasets are typically annotated in two stages: (1) determining the presence …
ObjectDilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting
Object counting aims to estimate the number of objects in images. The leading counting approaches focus on the single category counting task and achieve impressive performance. Note that there are multiple categories of …
ObjectObject CountingClickstream analysis for crowd-based object segmentation with confidence
With the rapidly increasing interest in machine learning based solutions for automatic image annotation, the availability of reference annotations for algorithm training is one of the major bottlenecks in the field. Crow…
Image SegmentationObjectSegmentationSemantic SegmentationThe devil is in the object boundary: towards annotation-free instance segmentation using Foundation Models
Foundation models, pre-trained on a large amount of data have demonstrated impressive zero-shot capabilities in various downstream tasks. However, in object detection and instance segmentation, two fundamental computer v…
Instance SegmentationObjectobject-detectionObject Detection+3